Generalized subsumption and its applications to induction and redundancy
Artificial Intelligence
Logic programming and databases
Logic programming and databases
Attributive concept descriptions with complements
Artificial Intelligence
Thoughts and afterthoughts on the 1988 Workshop on Principles of Hybrid Reasoning
AI Magazine - Reports from three of the 1990 Spring symposia and eight workshops held over the past two years
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
On the relative expressiveness of description logics and predicate logics
Artificial Intelligence
ACM Transactions on Database Systems (TODS)
Combining Horn rules and description logics in CARIN
Artificial Intelligence
{\cal A}{\cal L}-log: Integrating Datalog and Description Logics
Journal of Intelligent Information Systems
Equality and Domain Closure in First-Order Databases
Journal of the ACM (JACM)
What You Always Wanted to Know About Datalog (And Never Dared to Ask)
IEEE Transactions on Knowledge and Data Engineering
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Ontological Engineering
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Building rules on top of ontologies for the semantic web with inductive logic programming
Theory and Practice of Logic Programming
Conjunctive query answering for the description logic SHIQ
Journal of Artificial Intelligence Research
Query Answering for OWL-DL with rules
Web Semantics: Science, Services and Agents on the World Wide Web
On the decidability and complexity of integrating ontologies and rules
Web Semantics: Science, Services and Agents on the World Wide Web
Semantic and computational advantages of the safe integration of ontologies and rules
PPSWR'05 Proceedings of the Third international conference on Principles and Practice of Semantic Web Reasoning
Theory and Practice of Logic Programming
Inductive logic programming in databases: From datalog to $\mathcal{dl}+log}^{\neg\vee}$
Theory and Practice of Logic Programming
Logic programming languages for databases and the web
A 25-year perspective on logic programming
Nonmonotonic onto-relational learning
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
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ILP is a major approach to Relational Learning that exploits previous results in concept learning and is characterized by the use of prior conceptual knowledge. An increasing amount of conceptual knowledge is being made available in the form of ontologies, mainly formalized with Description Logics (DLs). In this paper we consider the problem of learning rules from observations that combine relational data and ontologies, and identify the ingredients of an ILP solution to it. Our proposal relies on the expressive and deductive power of the KR framework $\mathcal{DL}$+log that allows for the tight integration of DLs and disjunctive Datalogwith negation. More precisely we adopt an instantiation of this framework which integrates the DL $\mathcal{SHIQ}$ and positive Datalog. We claim that this proposal lays the foundations of an extension of Relational Learning, called Onto-Relational Learning, to account for ontologies.